Apparatus and method for identifying object

ABSTRACT

An artificial intelligence based object identifying apparatus and a method thereof which are capable of easily identifying a type of an object in an image using a small size learning model are disclosed. According to an embodiment of the present disclosure, an object identifying apparatus configured to identify an object from an image includes a receiver configured to receive the image, an image modifier configured to modify the received image by predetermined methods to generate a plurality of modified images, and an object determinator configured to apply the plurality of modified images to a neural network trained to identify an object from the image to obtain a plurality of identification results and determine a type of an object in the received image based on the plurality of identification results.

CROSS-REFERENCE TO RELATED APPLICATION

Pursuant to 35 U.S.C. § 119(a), this application claims the benefit ofearlier filing date and right of priority to Korean Patent ApplicationNo. 10-2019-0112662, filed on Sep. 11, 2019, the contents of which arehereby incorporated by reference herein in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to an apparatus and a method foridentifying an object which are capable of easily identifying a type ofan object in an image using a small size learning model (for example, aneural network).

2. Description of Related Art

Generally deep learning is defined as a set of machine learningalgorithms which attempts a high level of abstraction through acombination of several nonlinear transformations and is a field ofmachine learning which teaches computers human's way of thinking in alarge framework.

When there is data, many studies are conducted to represent the data tobe understood by the computer and apply the data to the learning and asa result of this effort, the development of the deep learning techniqueis actively performed to be applied to various fields (for example,image recognition, speech recognition, natural language processing).

However, in order to ensure an accuracy of the deep learning, big datais necessary in a learning step so that a capacity of a memory whichstores the big data is increased and thus a learning time during whichthe big data is used and a performance of a processor which trains usingthe big data are inevitably increased. Therefore, not only high power isnecessary, but also the cost is increased.

In the meantime, as a method for reducing a big data processing time inthe learning step, the related art suggests a method for shortening atime to set a region of interest (for example, a tumor portion in amedical image) by automatically setting the region of interest based ona reference set in the image data as big data, to consequently reduce alearning time during which the big data is used in the learning step.

However, according to the related art, even though a time to set theregion of interest in the image data as big data is shortened to shortenthe big data processing time in the learning step, it is difficult toreduce an amount of big data itself required in the learning step.Therefore, there is a limitation to shorten the learning time duringwhich the big data is used in the learning step and the problem causedby the learning using the big data cannot be solved.

RELATED ART DOCUMENT Patent Document

Patent Document: Korean Registered Patent Publication No. 10-1955919

SUMMARY OF THE INVENTION

An aspect of an embodiment of the present disclosure is to reduce acapacity of a memory which stores an image, a learning time to train aneural network and a performance of a processor (for example, a learner)which performs the training by training a small size neural networkwhich identifies an object from the image using a small amount of images(for example, a learning image), thereby driving the memory and theprocessor at a low power and saving the cost.

Further, another aspect of an embodiment of the present disclosure is toprecisely identify a type of an object in a received image by modifyingthe received image using a predetermined method (for example, at leastone of image rotation, removal of a noise in the image, image brightnessadjustment, and image size adjustment) to apply the modified image tothe neural network, thereby accurately identifying a type of the objectin the received image.

According to an aspect of the present disclosure, an object identifyingapparatus configured to identify an object from an image includes areceiver configured to receive the image; an image modifier configuredto modify the received image by predetermined methods to generate aplurality of modified images; and an object determinator configured toapply the plurality of generated modified images to a neural networktrained to identify an object from the image to obtain a plurality ofidentification results and determine a type of an object in the receivedimage based on the plurality of identification results.

According to an embodiment of the present disclosure, the image modifierincludes at least one of a rotation modifier configured to differentlyrotate the received image to generate a plurality of modified images; anoise modifier configured to differently remove a noise in the receivedimage to generate a plurality of modified images; a brightness modifierconfigured to differently adjust a brightness of the received image togenerate a plurality of modified images; and a size modifier configuredto differently adjust a size of the received image to generate aplurality of modified images.

According to an embodiment of the present disclosure, each of theplurality of identification results includes a type of an objectidentified from the modified image and a recognition rate which isprobability information indicating the probability that the objectidentified from the modified image is the identified type of object.

According to an embodiment of the present disclosure the objectdeterminator determines a type of an object having the highestrecognition rate among the plurality of identification results as a typeof an object in the received image.

According to an embodiment of the present disclosure when the highestrecognition rate is lower than a predetermined reference set value, theimage modifier differently adjusts a modification degree of modifyingusing the predetermined methods and regenerates the plurality ofmodified images based on the adjusted modification degree and the objectdeterminator redetermines the type of the object in the received image,based on the plurality of regenerated modified images.

According to an embodiment of the present disclosure, the predeterminedmethods include at least one of image rotation, noise removal in theimage, brightness adjustment of an image, and size adjustment of animage and the object determinator determines a type of an object havinga recognition rate which is equal to or higher than a set value, for theplurality of items, among the plurality of identification results, as atype of an object in the received image.

According to an embodiment of the present disclosure, the predeterminedmethods include at least one of image rotation, noise removal in theimage, brightness adjustment of an image, and size adjustment of animage and the image modifier adjusts a modification degree differentlyfor each item and generates a plurality of modified images based on theadjusted modification degree.

According to an embodiment of the present disclosure, the image modifiersets a modification unit and a modification range for the modificationdegree for every item and adjusts differently the modification degreebased on the set modification unit within the set modification range.

According to an embodiment of the present disclosure, the objectidentifying apparatus may further include a learner configured to trainthe neural network using the same or less number of learning imagescompared to a predetermined number.

According to another aspect of the present disclosure, an objectidentifying method for identifying an object from an image, receivingthe image, generating a plurality of modified images by modifying thereceived image by predetermined methods, obtaining a plurality ofidentification results by applying the plurality of generated modifiedimages to a neural network which is trained to identify an object froman image, and determining a type of an object in the received image,based on the plurality of identification results.

According to an embodiment of the present disclosure, the generating ofa plurality of modified images includes at least one of differentlyrotating the received image to generate a plurality of modified images,differently removing a noise in the received image to generate aplurality of modified images, differently adjusting a brightness of thereceived image to generate a plurality of modified images, anddifferently adjusting a size of the received image to generate aplurality of modified images.

According to an embodiment of the present disclosure, each of theplurality of identification results includes a type of an objectidentified from the modified image and a recognition rate which isprobability information indicating the probability that the objectidentified from the modified image is the identified type of object.

According to an embodiment of the present disclosure, the determining ofa type of an object in the received image includes, determining a typeof an object having the highest recognition rate among the plurality ofidentification results as a type of an object in the received image.

According to an embodiment of the present disclosure, the generating ofa plurality of modified images includes adjusting differently amodification degree of modifying using the predetermined methods toregenerate a plurality of modified images when the highest recognitionrate is lower than a predetermined reference set value and thedetermining of a type of an object in the received image includesredetermining a type of an object in the received image, based on theplurality of regenerated modified images.

According to an embodiment of the present disclosure, the predeterminedmethods include at least one of image rotation, noise removal in theimage, brightness adjustment of an image, and size adjustment of animage and the determining of a type of an object in the received imageincludes determining a type of an object having a recognition rate whichis equal to or higher than a set value, for the plurality of items amongthe plurality of identification results, as a type of an object in thereceived image.

According to an embodiment of the present disclosure, the predeterminedmethods include at least one of image rotation, noise removal in theimage, brightness adjustment of an image, and size adjustment of animage and the generating of a plurality of modified images includesadjusting a modification degree differently for each item and generatinga plurality of modified images based on the adjusted modificationdegree.

According to an embodiment of the present disclosure, the adjusting of amodification degree differently for each item and generating a pluralityof modified images based on the adjusted modification degree includessetting a modification unit and a modification range with respect to themodification degree for every item and differently adjusting themodification degree based on the set modification unit within the setmodification range.

According to an embodiment of the present disclosure, the objectidentifying method may further include, before receiving the image,training the neural network using the same or less number of learningimages compared to a predetermined number.

According to another aspect of the present disclosure, an objectidentifying apparatus configured to identify an object from an imageincludes a memory and one or more processors configured to executeinstructions stored in the memory and the one or more processors areconfigured to receive the image, generate a plurality of modified imagesby modifying the received image by predetermined methods, obtain aplurality of identification results by applying the plurality ofgenerated modified images to a neural network trained to identify anobject from an image and determine a type of an object in the receivedimage, based on the plurality of identification results.

In addition, another method and another system for implementing thepresent disclosure and a computer-readable recording medium having acomputer program stored therein to perform the method may be furtherprovided.

Other aspects and features as well as those described above will becomeclear from the accompanying drawings, claims, and the detaileddescription of the present disclosure.

According to the present disclosure, it is possible to reduce a capacityof a memory which stores an image, a learning time to train a neuralnetwork and a performance of a processor which performs the training bytraining a small size neural network which identifies an object from theimage using a small amount of images, thereby driving the memory and theprocessor at a low power and saving the cost.

According to the present disclosure, the received image is modifiedusing predetermined methods (at least one of image rotation, noiseremoval in the image, image brightness adjustment, and image sizeadjustment) to be applied to the small size neural network, therebyaccurately identifying the type of the object in the received image.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of the presentdisclosure will become apparent from the detailed description of thefollowing aspects in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a view illustrating an example of an AI system including an AIdevice including an object identifying apparatus according to anembodiment of the present disclosure, an AI server, and a networkconnecting the above-mentioned components;

FIG. 2 is a view illustrating a configuration of an AI device includingan object identifying apparatus according to an embodiment of thepresent disclosure;

FIG. 3 is a view illustrating a configuration of an object identifyingapparatus according to an embodiment of the present disclosure;

FIG. 4 is a view for explaining an example of training a neural networkin an object identifying apparatus according to an embodiment of thepresent disclosure;

FIG. 5 is a view illustrating an example of a configuration of an imagemodifier in an object identifying apparatus according to an embodimentof the present disclosure;

FIG. 6 is a view illustrating an example of identifying a type of anobject in an image in an object identifying apparatus according to anembodiment of the present disclosure;

FIG. 7 is a view illustrating another example of identifying a type ofan object in an image in an object identifying apparatus according to anembodiment of the present disclosure;

FIG. 8 is a view for explaining an example of an operation of an AIdevice to which an object identifying apparatus according to anembodiment of the present disclosure is applied; and

FIG. 9 is a flowchart illustrating an object identifying methodaccording to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The advantages and features of the present disclosure and ways toachieve them will be apparent by making reference to embodiments asdescribed below in detail in conjunction with the accompanying drawings.However, the description of particular example embodiments is notintended to limit the present disclosure to the particular exampleembodiments disclosed herein, but on the contrary, it should beunderstood that the present disclosure is to cover all modifications,equivalents and alternatives falling within the spirit and scope of thepresent disclosure. The example embodiments disclosed below are providedso that the present disclosure will be thorough and complete, and alsoto provide a more complete understanding of the scope of the presentdisclosure to those of ordinary skill in the art. In the interest ofclarity, not all details of the relevant art are described in detail inthe present specification in so much as such details are not necessaryto obtain a complete understanding of the present disclosure.

The terminology used herein is used for the purpose of describingparticular example embodiments only and is not intended to be limiting.As used herein, the singular forms “a,” “an,” and “the” may be intendedto include the plural forms as well, unless the context clearlyindicates otherwise. The terms “comprises,” “comprising,” “includes,”“including,” “containing,” “has,” “having” or other variations thereofare inclusive and therefore specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Furthermore, these terms such as “first,” “second,” and othernumerical terms, are used only to distinguish one element from anotherelement. These terms are generally only used to distinguish one elementfrom another.

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings. Like referencenumerals designate like elements throughout the specification, andoverlapping descriptions of the elements will not be provided.

FIG. 1 is a view illustrating an example of an AI system including an AIdevice including an object identifying apparatus according to anembodiment of the present disclosure, an AI server, and a networkconnecting the above-mentioned components.

Referring to FIG. 1, an artificial intelligence (AI) system 100 includesan AI device 110, an AI server 120, and a network 130.

Referring to FIG. 1, the AI device 100 may include an artificialintelligence based object identifying apparatus of the presentdisclosure and for example, include at least one of a robot, anautonomous vehicle, a communication terminal (for example, a mobilephone, a smart phone, or a tablet PC), and a home appliance (forexample, a robot cleaner).

Here, the artificial intelligence refers to a field of studyingartificial intelligence or a methodology to create the artificialintelligence and machine learning refers to a field of defining variousproblems treated in the artificial intelligence field and studying amethodology to solve the problems. In addition, machine learning may bedefined as an algorithm for improving performance with respect to a taskthrough repeated experience with respect to the task.

An artificial neural network (ANN) is a model used in machine learning,and may refer in general to a model with problem-solving abilities,composed of artificial neurons (nodes) forming a network by a connectionof synapses. The ANN may be defined by a connection pattern betweenneurons on different layers, a learning process for updating modelparameters, and an activation function for generating an output value.

The ANN may include an input layer, an output layer, and may selectivelyinclude one or more hidden layers. Each layer includes one or moreneurons, and the artificial neural network may include synapses thatconnect the neurons to one another. In an ANN, each neuron may output afunction value of an activation function with respect to the inputsignals inputted through a synapse, weight, and bias.

A model parameter refers to a parameter determined through learning, andmay include weight of synapse connection, bias of a neuron, and thelike. Moreover, hyperparameters refer to parameters which are set beforelearning in a machine learning algorithm, and include a learning rate, anumber of iterations, a mini-batch size, an initialization function, andthe like.

The objective of training an ANN is to determine a model parameter forsignificantly reducing a loss function. The loss function may be used asan indicator for determining an optimal model parameter in a learningprocess of an artificial neural network.

The machine learning may train an artificial neural network bysupervised learning.

Supervised learning may refer to a method for training an artificialneural network with training data that has been given a label. Inaddition, the label may refer to a target answer (or a result value) tobe guessed by the artificial neural network when the training data isinputted to the artificial neural network.

As a result, the artificial intelligence based object identifyingapparatus trains the artificial neural network using a machine learningalgorithm or requests a trained artificial neural network from the AIserver 120 to receive the trained artificial neural network from the AIserver 120. Further, when the image is received, the object identifyingapparatus may estimate a type of the object in the received image usingthe trained artificial neural network.

When the AI server 120 receives the request for the trained artificialneural network from the AI device 110, the AI server 120 may train theartificial neural network using the machine learning algorithm andprovide the trained artificial neural network to the AI device 110. TheAI server 120 may be composed of a plurality of servers to performdistributed processing. In this case, the AI server 120 may be includedas a configuration of a portion of the AI device 110, and may thusperform at least a portion of the AI processing together.

The network 130 may connect the AI device 110 and the AI server 120. Thenetwork 130 may include a wired network such as a local area network(LAN), a wide area network (WAN), a metropolitan area network (MAN), oran integrated service digital network (ISDN), and a wireless networksuch as a wireless LAN, a CDMA, Bluetooth®, or satellite communication,but the present disclosure is not limited to these examples. The network130 may also send and receive information using short distancecommunication and/or long distance communication. The short-rangecommunication may include Bluetooth®, radio frequency identification(RFID), infrared data association (IrDA), ultra-wideband (UWB), ZigBee,and Wi-Fi (wireless fidelity) technologies, and the long-rangecommunication may include code division multiple access (CDMA),frequency division multiple access (FDMA), time division multiple access(TDMA), orthogonal frequency division multiple access (OFDMA), andsingle carrier frequency division multiple access (SC-FDMA).

The network 130 may include connection of network elements such as ahub, a bridge, a router, a switch, and a gateway. The network 130 caninclude one or more connected networks, for example, a multi-networkenvironment, including a public network such as an internet and aprivate network such as a safe corporate private network. Access to thenetwork 130 may be provided through one or more wire-based or wirelessaccess networks. Furthermore, the network 130 may support the Internetof things (IoT) network for exchanging and processing informationbetween distributed elements such as things, 3G, 4G, Long Term Evolution(LTE), 5G communications, or the like.

FIG. 2 is a view illustrating a configuration of an AI device includingan object identifying apparatus according to an embodiment of thepresent disclosure.

Referring to FIG. 2, the AI device 200 includes a transceiver 210, aninput interface 220, a learning processor 230, a sensor 240, an outputinterface 250, a memory 260, a processor 270, and an object identifyingapparatus 280.

The transceiver 210 may transmit or receive data to/from externaldevices such as other AI device or AI server using wireless/wiredcommunication techniques. For example, the transceiver 210 may transmitor receive sensor data, user input, a learning model, a control signal,and the like with the external devices.

In this case, the communications technology used by the transceiver 210may be technology such as global system for mobile communication (GSM),code division multi access (CDMA), long term evolution (LTE), 5G,wireless LAN (WLAN), Wireless-Fidelity (Wi-Fi), Bluetooth™, radiofrequency identification (RFID), infrared data association (IrDA),ZigBee, and near field communication (NFC).

The input interface 220 may obtain various types of data. The inputinterface 220 may include a camera for inputting an image signal, amicrophone for receiving an audio signal, and a user input interface forreceiving information inputted from a user. Here, the camera or themicrophone is treated as a sensor so that a signal obtained from thecamera or the microphone may also be referred to as sensing data orsensor information.

The input interface 220 may obtain, for example, learning data for modellearning and input data used when output is obtained using a learningmodel. The input interface 220 may obtain raw input data. In this case,the processor 270 or the learning processor 230 may extract an inputfeature by preprocessing the input data.

The learning processor 230 may allow a model, composed of an artificialneural network to be trained using learning data. Here, the trainedartificial neural network may be referred to as a trained model. Thetrained model may be used to infer a result value with respect to newinput data rather than learning data, and the inferred value may be usedas a basis for a determination to perform an operation of classifyingthe detected hand motion. The learning processor 230 may perform AIprocessing together with a learning processor of the AI server.

Further, the learning processor 230 may include a memory which isintegrated or implemented in the AI device 200, but is not limitedthereto and may be implemented using an external memory directly coupledto the AI device or a memory sustained in the external device.

The sensor 240 may obtain at least one of internal information of the AIdevice 200, surrounding environment information of the AI device 200, oruser information by using various sensors. The sensor 240 may include aproximity sensor, an illumination sensor, an acceleration sensor, amagnetic sensor, a gyroscope sensor, an inertial sensor, an RGB sensor,an infrared (IR) sensor, a finger scan sensor, an ultrasonic sensor, anoptical sensor, a microphone, a light detection and ranging (LiDAR)sensor, radar, or a combination thereof.

The output interface 250 may generate a visual, auditory, or tactilerelated output. The output interface 250 may include a displayoutputting visual information, a speaker outputting auditoryinformation, and a haptic module outputting tactile information.

The memory 260 may store data supporting various functions of the AIdevice 200. For example, the memory 260 may store input data, thelearning data, the learning model, learning history, or the like,obtained from the input interface 220.

The memory 260 may serve to temporarily or permanently store dataprocessed by the processor 270. Here, the memory 260 may includemagnetic storage media or flash storage media, but the scope of thepresent disclosure is not limited thereto. The memory 260 as describedabove may include magnetic storage media or flash storage media, but thescope of the present disclosure is not limited thereto. The memory 260may include an internal memory and/or an external memory and may includea volatile memory such as a DRAM, a SRAM or a SDRAM, and a non-volatilememory such as one time programmable ROM (OTPROM), a PROM, an EPROM, anEEPROM, a mask ROM, a flash ROM, a NAND flash memory or a NOR flashmemory, a flash drive such as an SSD, a compact flash (CF) card, an SDcard, a Micro-SD card, a Mini-SD card, an XD card or memory stick, or astorage device such as a HDD.

The processor 270 may determine at least one executable operation of theAI device 200 based on information determined or generated by using adata analysis algorithm or a machine learning algorithm. In addition,the processor 270 may control components of the AI device 200 to performthe determined operation.

To this end, the processor 270 may request, retrieve, receive, or usedata of the learning processor 230 or the memory 260, and may controlcomponents of the AI device 200 to execute a predicted operation or anoperation determined to be preferable of the at least one executableoperation.

In this case, when it is required to be linked with the external deviceto perform the determined operation, the processor 270 may generate acontrol signal for controlling the external device and transmit thegenerated control signal to the corresponding external device.

The processor 270 obtains intent information about user input, and maydetermine a requirement of a user based on the obtained intentinformation. The processor 270 may obtain intent informationcorresponding to user input by using at least one of a speech to text(STT) engine for converting voice input into a character string or anatural language processing (NLP) engine for obtaining intentinformation of a natural language.

In an embodiment, the at least one of the STT engine or the NLP enginemay be composed of artificial neural networks, some of which are trainedaccording to a machine learning algorithm. In addition, the at least oneof the STT engine or the NLP engine may be trained by the learningprocessor 230, trained by a learning processor of an AI server, ortrained by distributed processing thereof.

The processor 270 collects history information including, for example,operation contents and user feedback on an operation of the AI device200, and stores the history information in the memory 260 or thelearning processor 230, or transmits the history information to anexternal device such as an AI server. The collected history informationmay be used to update a learning model.

The processor 270 may control at least some of components of the AIdevice 200 to drive an application stored in the memory 260.Furthermore, the processor 270 may operate two or more componentsincluded in the AI device 200 in combination with each other to drivethe application.

The object identifying apparatus 280 may include a receiver, a learner,a memory with a low capacity, an image modifier, and an objectdeterminator. Here, the receiver may be included in the input interface220, the learner may be included in the learning processor 230, and thememory with a low capacity may be included in the memory 260.

FIG. 3 is a view illustrating a configuration of an object identifyingapparatus according to an embodiment of the present disclosure.

Referring to FIG. 3, an object identifying apparatus 300 according to anembodiment of the present disclosure may include a receiver 310, alearner 320, a memory 330, an image modifier 340, and an objectdeterminator 350.

i) Learning Step

For example, the receiver 310 receives the same or less number ofplurality of learning images compared to a predetermined number, from anexternal camera of the object identifying apparatus 300 or an internalcamera (not illustrated) of the object identifying apparatus 300 tostore the learning images in the memory 330. Here, the plurality oflearning images is images obtained by photographing various types ofobjects (for example, a sofa, a fan, human, or a pet) by various methods(for example, varying a direction, a focal point, a brightness, and asize) to be utilized to train the neural network. The number ofplurality of learning images may be much smaller than the number oflearning images used in a learning step for deep learning of the relatedart.

The learner 320 trains the neural network using the plurality oflearning images stored in the memory 330 to identify the object from theimage and store the trained neural network in the memory 330. In thiscase, the learner 320 uses the relatively smaller number of plurality oflearning images compared to the number of images used in the learningstep for deep learning of the related art to train the neural network sothat a learning time (that is, a time to train the neural network toidentify the object from the image) may be shortened. In this case,since the neural network is trained based on the same or less number ofplurality of learning images compared to the predetermined number, theneural network may be a small size neural network (for example, a simplealgorithm).

The memory 330 may store the plurality of learning images and the neuralnetwork trained using the plurality of learning images. The number ofplurality of learning images is less than a predetermined number (forexample, 100) and the neural network has a small size, so that thememory 330 may have a small capacity as compared with the capacity ofthe memory used for the deep learning of the related art and may bedriven at a low power.

ii) Inferring Step

The receiver 310 may receive the image from, for example, the externalcamera or the internal camera.

The image modifier 340 may modify the received image using predeterminedmethods to generate a plurality of modified images. Here, thepredetermined methods may include at least one of image rotation, noiseremoval in the image, image brightness adjustment, and image sizeadjustment.

The image modifier 340 may adjust a modification degree differently foreach item and generate a plurality of modified images based on theadjusted modification degree. In this case, the image modifier 340 mayset a modification unit and a modification range for the modificationdegree for every item and adjust the modification degree differentlybased on the set modification unit within the set modification range.Here, a specific example of the modification unit, the modificationrange, and the modification degree will be described with reference toFIG. 5 below, for the convenience of description.

For example, the image modifier 340 may include at least one of arotation modifier which differently rotates the received image togenerate a plurality of modified images, a noise modifier whichdifferently removes noises in the received image to generate a pluralityof modified images, a brightness modifier which differently adjusts thebrightness of the received image to generate a plurality of modifiedimages, and a size modifier which differently adjusts the size of thereceived image to generate a plurality of modified images.

In the meantime, the image modifier 340 may differently adjust themodification degree (or modification unit, modification range) to modifyby the predetermined methods and regenerate the plurality of modifiedimages based on the adjusted modification degree (or modification unit,modification range) in accordance with control by the objectdeterminator 350.

The object determinator 350 applies the plurality of generated modifiedimages to a neural network (that is, a neural network stored in thememory 330) which is trained to identify the object from the image toobtain a plurality of identification results and determines a type ofthe object in the received image based on the plurality ofidentification results. Here, the object determinator 350 may obtain aplurality of identification results as many as the number of a pluralityof modified images.

The plurality of identification results, respectively, may include 1) atype of object (for example, “a sofa”, “a fan”, “human”, or “a pet”)identified from the modified image and 2) a recognition rate (forexample, 5% for the sofa, 88% for the fan, 6% for the human, and 1% forpet) which is probability information indicating the probability thatthe object identified from the modified image is the identified type ofthe object.

As an example of determining a type of an object in the received image,the object determinator 350 may determine a type of an object having thehighest recognition rate among the plurality of identification resultsas the type of the object in the received image.

In this case, when the highest recognition rate is lower than apredetermined reference set value (for example, 90%), the objectdeterminator 350 may allow the image modifier 340 to differently adjustthe modification degree to modify using the predetermined methods,regenerate a plurality of modified images based on the adjustedmodification degree, and redetermine the type of the object in thereceived image, based on the plurality of regenerated modified images.

As another example of determining a type of the object in the receivedimage, the object determinator 350 may determine a type of the objecthaving a recognition rate which is equal to or higher than apredetermined first set value with respect to the plurality of items,respectively, among the plurality of identification results, as the typeof the object in the received image. For example, when the first setvalue is 80% and among a plurality of identification results obtained byapplying the plurality of rotated modified images to the neural network,a first highest recognition rate is 88% for the fan, and among aplurality of identification results obtained by applying the pluralityof images modified by adjusting the size to the neural network, a secondhighest recognition rate is 81% for the fan, the object determinator maydetermine the type of object, “a fan” which shows first and secondrecognition rates which are higher than the first set value for twoitems (rotation and size) as the type of the object in the receivedimage.

As another example of determining a type of object in the receivedimage, the object determinator 350 modifies the images using one item ofthe predetermined methods to generate a plurality of modified images andwhen among the plurality of identification results obtained by applyingthe plurality of modified images to the neural network, the highestrecognition rate is equal to or higher than a second set value (here,the second set value may be higher than the first set value), a type ofan object having the highest recognition rate may be determined as thetype of the object in the image. For example, when the set value is 90%and among the plurality of identification results obtained by applyingthe plurality of rotated modified images to the neural network, thehighest recognition rate is “95% for the fan”, the object determinator350 may determine “a fan” which is the object having the highestrecognition rate in one item as a type of the object in the receivedimage.

As a result, the object determinator 350 may determine the type of theobject in the received image, in accordance with a set objectidentification reference, based on the plurality of identificationresults obtained by applying the plurality of modified images to theneural network. Here, as the set object identification reference, asmentioned above, for example, regardless of the item, a type of anobject having the highest recognition rate is determined as a type ofthe object in the received image (a first object identificationreference), a type of an object having a recognition rate which is equalto or higher than the first set value, for the plurality of items, isdetermined as a type of the object in the received image (a secondobject identification reference), or a type of an object having arecognition rate which is equal to or higher than the second set valuein one item is determined as a type of the object in the received image(a third object identification reference). In this case, when it isdifficult to determine the type of the object in the received imagebased on the set object identification reference (for example, the firstobject identification reference), the object determinator 350 may applyanother object identification reference (the first object identificationreference or the second object identification reference). Here, forexample, when the first object identification reference is applied, ifthere is a plurality of highest recognition rates, or when the secondobject identification reference is applied, if there is a plurality ofrecognition rates which is equal to or higher than the first set value,or when the third object identification reference is applied, if thereis a plurality of recognition rates which is equal to or higher than thesecond set value, it is difficult to determine the type of the object inthe received image.

As a result, in the learning step, even though the object identifyingapparatus 300 of the present disclosure trains a small size neuralnetwork which identifies the object from the image using the relativelyless number of learning images than the number of learning images of therelated art, in the inferring step, the received image is modified bypredetermined methods (for example, at least one of image rotation,noise removal in the image, image brightness adjustment, and image sizeadjustment) to apply the modified image to the neural network.Therefore, the type of the object in the received image may beaccurately identified.

In the meantime, the object identifying apparatus according to theembodiment of the present disclosure may include various configurations.For example, the object identifying apparatus is a device configured toidentify the object from the image and includes a memory and one or moreprocessors configured to execute instructions stored in the memory.Here, one or more processors may correspond to the receiver 310, thelearner 320, the image modifier 340, and the object determinator 350 andthe memory may correspond to the memory 330.

Specifically, the one or more processors may be configured to receivethe image, modify the received image using the predetermined methods togenerate a plurality of modified images, apply the plurality of modifiedimages to the neural network trained to identify the object from theimage to obtain a plurality of identification results, and determine thetype of the object in the received image based on the plurality ofidentification results.

FIG. 4 is a view for explaining an example of training a neural networkin an object identifying apparatus according to an embodiment of thepresent disclosure.

Referring to FIG. 4, the learner 410 in the object identifying apparatusmay train the neural network 430 to identify the object from the imageusing the plurality of learning images 420 stored in the memory andstore the trained neural network 430 in the memory.

Here, the plurality of learning images may be images obtained byphotographing various types of objects (for example, a sofa, a fan,human, a pet, etc.) by various methods (for example, varying adirection, a focal point, a brightness, a size, etc.). The number of aplurality of learning images (for example, 100) may be much smaller thanthe number of learning images (for example, 10000) used in a learningstep for deep learning of the related art.

FIG. 5 is a view illustrating an example of a configuration of an imagemodifier in an object identifying apparatus according to an embodimentof the present disclosure.

Referring to FIG. 5, the image modifier 510 in the object identifyingapparatus may modify the received image based on at least one of thepredetermined methods such as image rotation, noise removal in theimage, image brightness adjustment, and image size adjustment togenerate a plurality of modified images. In this case, the imagemodifier 510 sets a modification unit and a modification range for amodification degree for every item and adjusts the modification degreedifferently based on the set modification unit within the setmodification range.

Specifically, the image modifier 510 may include, for example, arotation modifier 511, a noise modifier 512, a brightness modifier 513,and a size modifier 514.

The rotation modifier 511 may rotate the received image 520 to generatea modified image. In this case, the rotation modifier 511 may adjustdifferently a modification degree (for example, 3°, 6°, 9°, . . . )based on a set modification unit (3°) within a modification range (forexample, 0° to 360°) set with regard to the rotation and generate aplurality of modified images 521 in accordance with the adjustedmodification degree.

The noise modifier 512 may remove the noise in the received image 520 togenerate a modified image. In this case, the noise modifier 512 mayadjust differently a modification degree (for example, 5%, 10%, 15%, . .. ) based on a set modification unit (5%) within a modification range(for example, 0% to 100% relative to the entire noise) set with regardto the noise and generate a plurality of modified images 522 inaccordance with the adjusted modification degree.

Further, the noise modifier 512 may employ various noise removingfilters to generate an image from which the noise is removed, by variousmethods and differently remove the noise while changing a parametervalue of the filter to generate a plurality of modified images.

The brightness modifier 513 may adjust the brightness of the receivedimage 520 to generate a modified image. In this case, the brightnessmodifier 513 may adjust differently the modification degree (forexample, 200 nit, 210 nit, 220 nit, . . . ) based on a set modificationunit (10 nit) within a modification range (for example, 200 to 500 nit)set with regard to a brightness (or luminance) and generate a pluralityof modified images 523 in accordance with the adjusted modificationdegree.

The size modifier 514 may adjust the size of the received image 520 togenerate a modified image. In this case, the size modifier 514 mayadjust differently the modification degree (for example, 0.2 times, 0.4times, 0.6 times, . . . ) based on a set modification unit (0.2 times)within a modification range (for example, 0.2 times to 5 times) set withregard to the size and generate a plurality of modified images 524 inaccordance with the adjusted modification degree.

Further, the image modifier 510 may adjust differently the modificationdegree for each of the image rotation, noise removal in the image, imagebrightness adjustment, and image size adjustment and modify the image520 based on the modification degree adjusted for every item to generatea plurality of modified images 521, 522, 523, and 524, but is notlimited thereto. As one example, the image modifier 510 may adjustdifferently the modification degree for all items and modify the image620 in accordance with the adjusted modification degree to generate aplurality of modified images. For example, the image modifier 510 maymodify the image 520 by applying rotation of 3°, 5% noise removal, 200nit brightness adjustment, 0.2 times size adjustment to generate firstmodified images and modify the image 520 by applying rotation of 6°, 10%noise removal, 210 nit brightness adjustment, 0.4 times size adjustmentto generate second modified images.

FIG. 6 is a view illustrating an example of identifying a type of anobject in an image in an object identifying apparatus according to anembodiment of the present disclosure.

Referring to FIG. 6, the image modifier 610 in the object identifyingapparatus may receive an image 620 and determine the type of the objectin the received image 620. Specifically, the image modifier 610 maymodify the image 620 by applying the predetermined methods (for example,at least one of image rotation, noise removal in the image, imagebrightness adjustment, and image size adjustment) as described withreference to FIG. 5, to generate a plurality of modified images 630.

The object determinator in the object identifying apparatus may be, forexample, a neural processing unit (NPU) and apply the plurality ofmodified images 630 to the neural network 640 which is trained toidentify the object from the image to obtain a plurality ofidentification results. Here, the plurality of identification resultsmay include a type of an object (for example, “a sofa”, “a fan”,“human”, or “a pet”) identified from the modified image and arecognition rate (for example, 5% for the sofa, 88% for the fan, 6% forthe human, and 1% for the pet) which is probability informationindicating the probability that the object identified from the modifiedimage is the identified type of the object.

Thereafter, the object determinator may determine a type (“a fan”) ofthe object having the highest recognition rate (for example, 88%) amongthe plurality of identification results obtained by applying theplurality of modified images 630 to the neural network 640 as the typeof the object in the received image.

FIG. 7 is a view illustrating another example of identifying a type ofan object in an image in an object identifying apparatus according to anembodiment of the present disclosure.

Referring to FIG. 7, an image modifier 710 in the object identifyingapparatus may receive an image 720 and determine a type of an object inthe received image 720. Specifically, the image modifier 710 may modifythe image 720 by applying the predetermined methods (for example, atleast one of image rotation, noise removal in the image, imagebrightness adjustment, and image size adjustment) as described withreference to FIG. 5, to generate a plurality of modified images 730. Inthis case, the image modifier 710 may differently rotate the image 720to generate a plurality of modified images 731 and differently removethe noise from the image 720 to generate a plurality of modified images732. Further, the image modifier 710 may differently adjust a brightnessof the image 720 to generate a plurality of modified images 733 anddifferently adjust a size of the image 720 to generate a plurality ofmodified images 734.

For example, as an identification result obtained by applying a modifiedimage 731-1 obtained by rotating the image 720 by 3° to a neural network740, the object determinator may obtain types of objects identified fromthe rotated modified image, that is, “a sofa”, “a fan”, “human”, and “apet” and a recognition rates (for example, 5% for the sofa, 88% for thefan, 6% for the human, and 1% for the pet) which is probabilityinformation indicating the probability that an object identified fromthe rotated modified image 731-1 is the identified type of object (thatis, the probability that the object identified from the rotated modifiedimage is “a sofa”, “a fan”, “human”, or “a pet”). Further, the objectdeterminator may apply a modified image 731-2 obtained by rotating theimage 720 by 6° to the neural network 740 to obtain an identificationresult (for example, recognition rates are 2% for the sofa, 90% for thefan, 5% for the human, and 2% for the pet). In this case, anidentification result with regard to the modified image 731-1 obtainedby rotating by 3° and an identification result with regard to themodified image 731-2 by rotating by 6° may be different from each other.

As an identification result obtained by applying a modified image 732-1obtained by removing 5% of noise from the image 720 to the neuralnetwork 740, the object determinator may obtain types of objectsidentified from the image modified by removing the noise, that is, “asofa”, “a fan”, “human”, and “a pet” and a recognition rate (forexample, 7% for the sofa, 27% for the fan, 9% for the human, and 3% forthe pet) which is probability information indicating the probabilitythat an object identified from the image 732-1 obtained by removing anoise is the identified type of object. Further, the object determinatormay apply a modified image 732-2 obtained by removing 10% of noise fromthe image 720 to the neural network 740 to obtain an identificationresult (for example, 7% for the sofa, 30% for the fan, 8% for the human,and 5% for the pet). In this case, an identification result with regardto the modified image 732-1 obtained by removing 5% of noise and anidentification result with regard to the modified image 732-2 obtainedby removing 10% of noise may be different from each other.

As an identification result obtained by applying an image 733-1 modifiedby adjusting a brightness of the image 720 to 200 nit to the neuralnetwork 740, the object determinator may obtain types of objectsidentified from the image 733-1 modified by adjusting the brightness,that is, “a sofa”, “a fan”, “human”, and “a pet” and a recognition rate(for example, 11% for the sofa, 21% for the fan, 7% for the human, and9% for the pet) which is probability information indicating theprobability that an object identified from the image 733-1 modified byadjusting the brightness is the identified type of object. Further, theobject determinator may apply the image 733-2 modified by adjusting thebrightness of the image 720 to 210 nit to the neural network 740 toobtain an identification result (for example, 20% for the sofa, 19% forthe fan, 15% for the human, and 10% for the pet). In this case, anidentification result with regard to the image 733-1 modified byadjusting the brightness to 200 nit and an identification result withregard to the image 733-2 modified by adjusting the brightness to 210nit may be different from each other.

Further, as an identification result obtained by applying an image 734-1modified by adjusting a size of the image 720 by 0.2 times to the neuralnetwork 740, the object determinator may obtain types of objectsidentified from the image 734-1 modified by adjusting the size, that is,“a sofa”, “a fan”, “human”, and “a pet” and a recognition rate (forexample, 2% for the sofa, 37% for the fan, 5% for the human, and 2% forthe pet) which is probability information indicating the probabilitythat an object identified from the image 734-1 modified by adjusting thesize is the identified type of object. Further, the object determinatormay apply the image 734-2 modified by adjusting the size of the image720 by 0.4 times to the neural network 740 to obtain the identificationresult (for example, 5% for the sofa, 81% for the fan, 6% for the human,and 9% for the pet). In this case, an identification result with regardto the image 734-1 modified by adjusting the size by 0.2 times and anidentification result with regard to the image 734-2 modified byadjusting the size by 0.4 times may be different from each other.

Thereafter, the object determinator may determine a type of the objecthaving the highest recognition rate among the plurality ofidentification results obtained by applying the plurality of modifiedimages 730 to the neural network 740 as the type of the object in thereceived image, but is not limited thereto.

As another example, the object determinator may determine a type of theobject having recognition rates, each of which is equal to or higherthan the first set value, for the plurality of items, among theplurality of identification results obtained by applying the pluralityof modified images 730 to the neural network 740, as the type of theobject in the received image.

For example, the object determinator may select the highest firstrecognition rate (for example, 88% for the fan) from the plurality ofidentification results obtained by applying the plurality of rotatedmodified images 731 to the neural network 740 and select the highestsecond recognition rate (for example, 30% for the fan) from theplurality of identification results obtained by applying the pluralityof images 732 modified by removing the noise to the neural network 740.Further, the object determinator may select the highest thirdrecognition rate (for example, 20% for the fan) from the plurality ofidentification results obtained by applying the plurality of images 733modified by adjusting the brightness to the neural network 740 andselect the highest fourth recognition rate (for example, 81% for thefan) from the plurality of identification results obtained by applyingthe plurality of images 734 modified by adjusting the size to the neuralnetwork 740.

For example, when the first set value is 80%, since the selected firstand fourth recognition rates are equal to or higher than the first setvalue, the object determiner may determine “a fan” (which is a type ofobject having first and second recognition rates equal to or higher thanthe first set value in two items (rotation and size)) as a type of anobject in the image 720.

As another example, the object determinator may generate the pluralityof modified images by modifying the image 720 using one of thepredetermined methods and among the plurality of identification resultsobtained by applying the plurality of modified images to the neuralnetwork 740, when the highest recognition rate is equal to or higherthan a second set value (the second set value may be higher than thefirst set value), determine the type of the object having the highestrecognition rate as the type of the object in the image 720. Forexample, when the second set value is 90% and among the plurality ofidentification results obtained by applying the plurality of rotatedmodified images 731 to the neural network 740, the highest firstrecognition rate is 95% for the fan, the object determinator maydetermine “a fan” which is the object having the highest recognitionrate in one item as a type of the object in the image 720.

FIG. 8 is a view for explaining an example of an operation of an AIdevice to which an object identifying apparatus according to anembodiment of the present disclosure is applied.

Referring to FIG. 8, the object identifying apparatus may be applied tothe AI device. For example, the object identifying apparatus may beapplied to a robot cleaner 810 as an AI device.

The robot cleaner 810 may include, for example, a camera and if anobject is sensed based on an image photographed by the camera during themovement, the robot cleaner 810 may determine a type of the object inthe image using the object identifying apparatus installed therein. Therobot cleaner 810 may control the motion (for example, a direction, aspeed, and a distance of the movement) based on the determined type ofthe object.

For example, when a type of the object in a position (or a direction) tomove is an immovable object (for example, a fan or a sofa), the robotcleaner 810 may change the movement direction to avoid the object.

In contrast, when the type of the object in the position to move is amoving object (for example, human or a pet), the robot cleaner 810reduces the movement speed or pauses and then if the object moves to aposition different from the position to move, increases the movementspeed or move to a position to move again.

FIG. 9 is a flowchart illustrating an object identifying methodaccording to an embodiment of the present disclosure. Here, an objectidentifying apparatus which implements the object identifying method ofthe present disclosure receives the same or less number of plurality oflearning images compared to a predetermined number and trains a smallsize neural network in advance to identify the object from the imageusing the plurality of received learning images. Here, the plurality oflearning images are images obtained by photographing various types ofobjects (for example, a sofa, a fan, human, or a pet) by various methods(for example, varying a direction, a focal point, a brightness, and asize).

Referring to FIG. 9, in step S910, for example, the object identifyingapparatus may receive an image from an external camera of the objectidentifying apparatus or an internal camera of the object identifyingapparatus.

In step S920, the object identifying apparatus may modify the receivedimage by predetermined methods to generate a plurality of modifiedimages. For example, the object identifying apparatus may differentlyrotate the received image to generate a plurality of modified images,differently remove the noise in the received image to generate aplurality of modified images, differently adjust the brightness of thereceived image to generate a plurality of modified images, ordifferently adjust the size of the received image to generate aplurality of modified images.

That is, the predetermined methods may include at least one of imagerotation, noise removal in the image, brightness adjustment of an image,and size adjustment of an image. The object identifying apparatus mayadjust a modification degree differently for each item and generate aplurality of modified images based on the adjusted modification degree.In this case, the object identifying apparatus may set a modificationunit and a modification range for the modification degree for every itemand adjust the modification degree differently based on the setmodification unit within the set modification range.

In step S930, the object identifying apparatus may apply the pluralityof modified images to a neural network trained to identify the objectfrom the image to obtain a plurality of identification results. Here,the plurality of identification results, respectively, may include atype of object (for example, “a sofa”, “a fan”, “human”, or “a pet”)identified from the modified images and a recognition rate (for example,5% for the sofa, 88% for the fan, 6% for the human, and 1% for the pet)which is probability information indicating the probability that theobject identified from the modified image is the identified type ofobject.

In step S940, the object identifying apparatus may determine the type ofthe object in the received image based on the plurality ofidentification results. In this case, the object identifying apparatusmay determine the type of object having the highest recognition rate,among the plurality of identification results, as the type of the objectin the received image.

In the meantime, when the highest recognition rate is lower than thepredetermined reference set value, the object identifying apparatus maydifferently adjust the modification degree of modifying using thepredetermined methods to regenerate a plurality of modified images andmay redetermine the type of the object in the received image, based onthe plurality of regenerated modified images.

As another example of determining a type of object in the receivedimage, the object identifying apparatus may determine a type of objecthaving recognition rates, each of which is equal to or higher than apredetermined first set value, with respect to the plurality of items,among the plurality of identification results, as the type of object inthe received image. For example, when the first set value is 80% andamong a plurality of identification results obtained by applying theplurality of rotated modified images to the neural network, a firsthighest recognition rate is, for example, 88% for the fan, and among aplurality of identification results obtained by applying the pluralityof images modified by adjusting the size to the neural network, a secondhighest recognition rate is, for example, 81% for the fan, the objectidentifying apparatus may determine the type of object, “a fan” whichshows first and second recognition rates which are higher than the firstset value for two items (rotation and size) as a type of the object inthe received image.

As another example of determining the type of the object in the receivedimage, the object identifying apparatus generates the plurality ofmodified images using one of the predetermined methods and among theplurality of identification results obtained by applying the pluralityof modified images to the neural network, when the highest recognitionrate is equal to or higher than a predetermined second set value (here,the second set value may be higher than the first set value), determinesthe type of the object having the highest recognition rate as a type ofthe object in the image. For example, when the second set value is 90%and among the plurality of identification results obtained by applyingthe plurality of rotated modified images to the neural network, thehighest recognition rate is 95% for the fan, the object identifyingapparatus may determine “a fan” which is the object having the highestrecognition rate in one item as a type of the object in the receivedimage.

Embodiments according to the present disclosure described above may beimplemented in the form of computer programs that may be executedthrough various components on a computer, and such computer programs maybe recorded in a computer-readable medium. Examples of thecomputer-readable media include, but are not limited to: magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROM disks and DVD-ROM disks; magneto-optical media such asfloptical disks; and hardware devices that are specially configured tostore and execute program codes, such as ROM, RAM, and flash memorydevices.

Meanwhile, the computer programs may be those specially designed andconstructed for the purposes of the present disclosure or they may be ofthe kind well known and available to those skilled in the computersoftware arts. Examples of program code include both machine codes, suchas produced by a compiler, and higher level code that may be executed bythe computer using an interpreter.

As used in the present disclosure (especially in the appended claims),the singular forms “a,” “an,” and “the” include both singular and pluralreferences, unless the context clearly states otherwise. Also, it shouldbe understood that any numerical range recited herein is intended toinclude all sub-ranges subsumed therein (unless expressly indicatedotherwise) and accordingly, the disclosed numeral ranges include everyindividual value between the minimum and maximum values of the numeralranges.

Operations constituting the method of the present disclosure may beperformed in appropriate order unless explicitly described in terms oforder or described to the contrary. The present disclosure is notnecessarily limited to the order of operations given in the description.All examples described herein or the terms indicative thereof (“forexample,” etc.) used herein are merely to describe the presentdisclosure in greater detail. Therefore, it should be understood thatthe scope of the present disclosure is not limited to the exampleembodiments described above or by the use of such terms unless limitedby the appended claims. Therefore, it should be understood that thescope of the present disclosure is not limited to the exampleembodiments described above or by the use of such terms unless limitedby the appended claims. Also, it should be apparent to those skilled inthe art that various alterations, substitutions, and modifications maybe made within the scope of the appended claims or equivalents thereof.

Therefore, technical ideas of the present disclosure are not limited tothe above-mentioned embodiments, and it is intended that not only theappended claims, but also all changes equivalent to claims, should beconsidered to fall within the scope of the present disclosure.

What is claimed is:
 1. An object identifying apparatus configured to identify an object from an image, the apparatus comprising: a memory; and one or more processors configured to execute instructions stored in the memory, wherein the one or more processors are configured to: receive the image; modify the received image by predetermined methods to generate a plurality of modified images; and apply the plurality of generated modified images to a neural network trained to identify an object from the image to obtain a plurality of identification results and determine a type of an object in the received image based on the plurality of identification results.
 2. The object identifying apparatus according to claim 1, wherein modifying the received image comprises at least one of: differently rotating the received image to generate the plurality of modified images; differently removing a noise in the received image to generate a plurality of modified images; differently adjusting a brightness of the received image to generate a plurality of modified images; and differently adjusting a size of the received image to generate a plurality of modified images.
 3. The object identifying apparatus according to claim 1, wherein each of the plurality of identification results includes a type of an object identified from the modified image and a recognition rate which is probability information indicating a probability that the object identified from the modified image is the identified type of object.
 4. The object identifying apparatus according to claim 3, wherein the determining a type of an object in the received image comprises determining a type of an object having the highest recognition rate among the plurality of identification results as a type of an object in the received image.
 5. The object identifying apparatus according to claim 4, wherein the one or more processors are further configured to: when the highest recognition rate is lower than a predetermined reference set value, differently adjust a modification degree of modifying using the predetermined methods and regenerate the plurality of modified images based on the adjusted modification degree, and redetermine the type of the object in the received image, based on the plurality of regenerated modified images.
 6. The object identifying apparatus according to claim 3, wherein the predetermined methods include at least one of image rotation, noise removal in the image, brightness adjustment of an image, and size adjustment of an image and the one or more processors are configured to determine a type of an object having recognition rates which are equal to or higher than a set value, respectively, for the plurality of items, among the plurality of identification results, as a type of an object in the received image.
 7. The object identifying apparatus according to claim 1, wherein the predetermined methods include at least one of image rotation, noise removal in the image, brightness adjustment of an image, and size adjustment of an image and the one or more processors are configured to adjust a modification degree differently for each item and generate a plurality of modified images based on the adjusted modification degree.
 8. The object identifying apparatus according to claim 7, wherein adjusting the modification degree comprises setting a modification unit and a modification range with respect to the modification degree for every item and differently adjusting the modification degree based on the set modification unit within the set modification range.
 9. The object identifying apparatus according to claim 1, wherein the one or more processors are further configured to: train the neural network using the same or less number of learning images compared to a predetermined number.
 10. An object identifying method for identifying an object from an image, the method comprising: receiving the image; generating a plurality of modified images by modifying the received image by predetermined methods; obtaining a plurality of identification results by applying the plurality of generated modified images to a neural network which is trained to identify an object from an image; and determining a type of an object in the received image, based on the plurality of identification results.
 11. The object identifying method according to claim 10, wherein the generating of a plurality of modified images includes at least one of differently rotating the received image to generate the plurality of modified images; differently removing a noise in the received image to generate a plurality of modified images; differently adjusting a brightness of the received image to generate a plurality of modified images; and differently adjusting a size of the received image to generate a plurality of modified images.
 12. The object identifying method according to claim 10, wherein each of the plurality of identification results includes a type of an object identified from the modified image and a recognition rate which is probability information indicating a probability that the object identified from the modified image is the identified type of object.
 13. The object identifying method according to claim 12, wherein the determining of a type of an object in the received image includes: determining a type of an object having the highest recognition rate among the plurality of identification results as a type of an object in the received image.
 14. The object identifying method according to claim 13, wherein the generating of a plurality of modified images includes: adjusting differently a modification degree of modifying using the predetermined methods to regenerate a plurality of modified images when the highest recognition rate is lower than a predetermined reference set value, and the determining of a type of an object in the received image includes: redetermining a type of an object in the received image, based on the plurality of regenerated modified images.
 15. The object identifying method according to claim 12, wherein the predetermined methods include at least one of image rotation, noise removal in the image, brightness adjustment of an image, and size adjustment of an image and the determining of a type of an object in the received image includes: determining a type of an object having recognition rates which are equal to or higher than a set value, respectively, for the plurality of items among the plurality of identification results, as a type of an object in the received image.
 16. The object identifying method according to claim 10, wherein the predetermined methods include at least one of image rotation, noise removal in the image, brightness adjustment of an image, and size adjustment of an image and the generating of a plurality of modified images includes: adjusting a modification degree differently for each item and generating a plurality of modified images based on the adjusted modification degree.
 17. The object identifying method according to claim 16, wherein the adjusting of a modification degree differently for each item and generating a plurality of modified images based on the adjusted modification degree includes: setting a modification unit and a modification range with respect to the modification degree for every item; and differently adjusting the modification degree based on the set modification unit within the set modification range.
 18. The object identifying method according to claim 10, further comprising: before receiving the image, training the neural network using the same or less number of learning images compared to a predetermined number. 